Implementing Performance Measurement Programs: The Blue Cross Blue Shield of Massachusetts Perspective

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Implementing Performance Measurement Programs: The Blue Cross Blue Shield of Massachusetts Perspective Dana Gelb Safran, Sc.D. Senior Vice President Performance Measurement and Improvement Massachusetts Health Data Consortium 2012 Chief Information Officer Retreat 17 May 2012

Advancing Quality, Outcomes and Affordability: Role of Performance Measurement and Reporting Programs Reporting to Providers Member Incentives & Benefits Provider Incentives Reporting to Public 2

Guiding Principles in Selecting Performance Measures for High Stakes Use (Adopted Jan 2007) Wherever possible, our measures should be drawn from nationally accepted standard measure sets. The measure must reflect something that is broadly accepted as clinically important. There must be empirical evidence that the measure provides stable and reliable information at the level at which it will be reported (i.e. individual, site, group, or institution) with available sample sizes and data sources. There must be sufficient variability on the measure across providers (or at the level at which data will be reported) to merit attention. The must be empirical evidence that the level of the system that will be held accountable (clinician, site, group, institution) accounts for substantial system-level variance in the measure. Providers should be exposed to information about the development and validation of the measures and given the opportunity to view their own performance, ideally for one measurement cycle, before the data are used for high stakes purposes. 3

Staged Development and Use of Performance Measures Time 0 Time 1 Phase I Development & Testing Phase II Initial Large-Scale Implementation Phase III Implementing Measures for High Stakes Purposes Initial measure implementation. Final measure validation/testing. Stakeholder buy-in. Initial QI cycle. P4P Public Reporting Tiering 4

Patient Exper. Outcomes Process Experimental Aggregate Score 2.3 AQC Measures - Illustration Only - Not Actual Provider Scores Ambulatory Measures Hospital Measures Measure Score Weight Measure Score Weight Depression AMI 1 Acute Phase Rx 2.5 1.0 1 ACE/ARB for LVSD 2.0 1.0 2 Continuation Phase Rx 1.5 1.0 2 Aspirin at arrival 2.5 1.0 Diabetes 3 Aspirin at discharge 1.5 1.0 3 HbA1c Testing (2X) 3.0 1.0 4 Beta Blocker at arrival 1.5 1.0 4 Eye Exams 1.0 1.0 5 Beta Blocker at discharge 1.3 1.0 5 Nephropathy Screening 1.2 1.0 6 Smoking Cessation 1.0 1.0 Cholesterol Management Heart Failure 6 Diabetes LDL-C Screening 2.8 1.0 7 ACE LVSD 1.3 1.0 7 Cardiovascular LDL-C Screening 2.1 1.0 8 LVS function Evaluation 1.0 1.0 9 Discharge instructions 1.8 1.0 8 Breast Cancer Screening 1.2 1.0 10 Smoking Cessation 3.0 1.0 9 Cervical Cancer Screening 1.3 1.0 Pneumonia 10 Colorectal Cancer Screening 2.4 1.0 11 Flu Vaccine 2.5 1.0 Preventive Screening/Treatment 12 Pneumococcal Vaccination 2.9 1.0 Chlamydia Screening 13 Antibiotics w/in 4 hrs 1.4 1.0 11 Ages 16-20 3.1 0.5 14 Oxygen assessment 1.0 1.0 12 Ages 21-25 1.8 0.5 15 Smoking Cessation 3.1 1.0 Pedi: Testing/Treatment 16 Antibiotic selection 3.0 1.0 13 Upper Respiratory Infection (URI) 1.6 1.0 17 Blood culture 3.5 1.0 14 Pharyngitis 1.4 1.0 Surgical Infection Pedi: Well-visits 18 Antibiotic received 1.3 1.0 15 < 15 months 2.6 1.0 19 Received Appropriate Preventive Antibiotic( 1.4 1.0 16 3-6 Years 2.0 1.0 20 Antibiotic discontinued 3.0 1.0 17 Adolescent Well Care Visits 1.5 1.0 Diabetes 21 In-Hospital Mortality - Overall 3.0 1.0 18 HbA1c in Poor Control 3.2 3.0 22 Wound Infection 2.1 1.0 19 LDL-C Control (<100mg) 2.4 3.0 23 Select Infections due to Medical Care 2.8 1.0 Hypertension 24 AMI after Major Surgery 2.4 1.0 20 Controlling High Blood Pressure 1.3 3.0 25 Pneumonia after Major Surgery 3.4 1.0 Cardiovascular Disease 26 Post-Operative PE/DVT 2.0 1.0 21 LDL-C Control (<100mg) 2.4 3.0 27 Birth Trauma - injury to neonate 1.0 1.0 28 Obstetrics Trauma-vaginal w/o instrument 1.5 1.0 Patient Experiences (C/G CAHPS/ACES) - Adult 3 Hospital Patient Experience (H-CAHPS) Measures 22 Communication Quality 1.9 1.0 29 Communication with Nurses 4.0 1.0 23 Knowledge of Patients 1.9 1.0 30 Communication with Doctors 3.0 1.0 24 Integration of Care 2.1 1.0 31 Responsiveness of staff 2.5 1.0 25 Access to Care 2.4 1.0 32 Discharge Information 2.8 1.0 Patient Experiences (C/G CAHPS/ACES) - Pediatric 3 26 Communication Quality 1.0 1.0 27 Knowledge of Patients 1.5 1.0 28 Integration of Care 2.5 1.0 29 Access to Care 2.8 1.0 30 Experimental Measure A 5.0 1.0 33 Experimental Measure C 5.0 1.0 31 Experimental Measure B 5.0 1.0 Weighted Ambulatory Score 2.2 Weighted Hospital Score 2.3

Patient Experience Outcomes Process Data Sources: Performance Incentive Measures Ambulatory Measures Hospital Measures BCBSMA Commercial Claims CMS Hospital Compare Provider reported clinical data All-payer hospital discharge dataset (Commonwealth of MA) Statewide survey administered by Massachusetts Health Quality Partners CMS Hospital Compare 6

Data Sources: Collecting Outcomes from Providers 7

Performance Incentive Model: Rewarding Both Performance and Performance Improvement % Payout Performance Payment Model 10% 8% 9.0% 10.0% 6% 5.0% 4% 3.0% 2% 2.0% 0% 1.0 2.0 3.0 4.0 5.0 Performance Score 8

Optimal Care AQC Improving Preventive and Chronic Care The 2009 AQC cohort continues to demonstrate success improving quality achieving benchmarks significantly higher than non-aqc peers. The 2010 AQC cohort made significant quality improvements in year-1 of their contract (2009 vs. 2010). Preventive Screenings Chronic Care Management 5 4 2009 AQC Cohort 2010 AQC Cohort Non-AQC 2009 AQC Cohort 3.9 3.6 2010 AQC Cohort Non-AQC 3.3 3.3 3 2 2.3 2.7 1.8 2.5 1.7 1.8 2.5 1.7 1.7 2.6 2.7 2 2.1 2.2 1.9 1 0.5 0.9 1.2 1.1 1.1 0 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 9

Rate of ARB Use per 100 Episodes with ACE-I and/or ARB Identifying & Addressing Clinically Wasteful Care Since 1970s, Wennberg et al. have called attention to unexplained practice pattern variations using maps Dr. Howard Beckman developed an analytic approach that makes the information clinically meaningful and actionable (Greene RA, et al. Health Affairs 2008; w250-259) Clinically-specific, specialty-specific 100 Rate = Episodes with ARB / Episodes with ACE-I and/or ARB approach to displaying practice 90 pattern variations engages 80 physician leaders and front line 70 physicians in addressing clinical The 12 primary care physicians in this group have waste 60 rates of ARB use ranging from 13% to 55%. Referral tendencies, use of 9 physicians have rates above the network average. 50 procedures, use of diagnostics, 40 use of therapeutics 30 This is a slow but critical process Payment models that create 20 accountability for resource use 10 (e.g., global budget) give a strong 0 incentive to act on these data. 1 355 709 1063 1417 1771 2125 2479 2833 Individual Primary Care Physicians (N=3178) 10

Summary A clear, consistent and comprehensive set of principles to guide the appropriate use of performance measures has proven extremely valuable Staging the process of measure development to include widespread testing and feedback prior to high stakes use is important empirically, practically and politically Rapid and substantial performance improvement appears to follow when the stage is set with: Meaningful financial incentives for improvement on measures that are well accepted, widely validated and clinically important Ongoing and timely data to inform improvement efforts Organizational structure and leadership commitment to the goals Under a payment model that creates accountability for resource use (e.g., global budget), cost and efficiency measures do not need to meet criteria for high stakes use. Measurement is needed to support accountability and success but not for high stakes Incentives for improvement on this domain are built into the payment model While alignment of measures across payers has appeal, payer-provider relationships remain a valuable mechanism for continued innovation and testing of new measures And use of all-payer data is generally not desirable for accounts (customers) 11

For More Information dana.safran@bcbsma.com 12